Learning from demonstrations. Some real-world settings may restrict the learning process to logged data. This constraint is due to the difficulty of creating accurate simulators or even safety constraints. For example, an autonomous vehicle cannot learn by interacting with the environment through trial-error, both for safety and cost reasons. For this, offline RL [1]} approaches such as BCQ [2]}, [3]}, REM [4]}, Conservative Q-learning [5]}, and Averaged-DQN [6]} were designed to increase the efficiency of exploiting previously collected data, mainly tackling the overestimation phenomena [7]}. This data can be, for instance, obtained by human operation in the real-world system, as in the present case. In [8]}, we show that a large dataset is capable of producing policies through offline approaches that can outperform the behavioral policy. Thus, the present dataset can serve as a benchmark for offline RL approaches. Finally, these expert demonstrations can guide the agent's actions through imitation learning [9]}, used to build models [10]}, [11]}, or boost the learning process [12]}.
